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There he is. You met him not four months ago at a function just like this. You recognize the face. But his name . . . what is his name?! Suddenly, he is upon you, remembering your name with ease. “Heeeeeeyyyyyy!” is all you can muster. This elongated greeting serves two vital purposes. First, it’s a time-killer. You could still remember the name. Second, it is intended to communicate a level of familiarity, suggesting that your relationship has reached the point that transcends the use of names. He’s not buying it. After some uncomfortable banter, the acquaintance bids your squirming, embarrassed self good-bye. You’ve done it again, Sherlock. The study of memory is nuanced and ever evolving, but if there is one truth within the greater field, it is this: Effective explicit recall depends upon proper elaborative encoding. Unless a name is actively rehearsed or a mnemonic strategy is invoked, recall is likely to be poor. For example, I once had the greatest difficulty remembering the name of a woman I had met named Gisa, until it later occurred to me that her name rhymed with both a leaning tower and a credit card. When we meet someone for the first time, other thoughts likely impede our ability to invoke such strategies. We may be busy assessing the other person’s style of dress. Maybe they resemble someone else we know. Perhaps we are wondering what they think of us. These processes effectively comprise “distractor tasks” serving to hinder the proper encoding of the one bit of information that we must come away with—the name. Here, a very simple task becomes an all too common social faux pas. Our brains, as impressive as they are, are often no match for the social contexts in which they find themselves. In this issue, Paller and colleagues (Paller et al. 2003) offer human neuroimaging data identifying neural circuits involved when subjects explicitly attempted to remember biographical information (including names) related to pictures of faces they had previously seen. Subjects in this study were shown numerous pictures of faces. During presentation of a subset of these faces, subjects also heard a voice describing the name and a biographical detail of the presented face. Subjects were scanned during subsequent presentations of these faces (this time, no auditory biographical information was presented) and told to retrieve this information for the relevant subset of faces. Most notably, this paper reports that retrieval of this biographical information was related to activation of the medial temporal lobe, including convincing involvement of the hippocampus. In addition, the authors present a parallel ERP study that offers data concerning the timing of brain responses associated with this task. These data, together with other studies in which subjects were presented with face–name pairs (Campanella et al. 2001; Sperling et al. 2001), provide an important starting point for the study of this very human ability. In addition, the present study also reports involvement of the prefrontal cortex, suggesting that these data may also be relevant to more basic human studies of source memory (see, e.g., Dobbins et al. 2002), that is, the recall of contextual details associated with a previously encountered stimulus (see recommended reading in Box 1). As with any interesting initial study, there is, of course, more that we would like to know. For example, because subjects covertly retrieved biographical information during scanning, we cannot know with absolute certainty which activations presented here are related to the explicit retrieval of this information. Indeed, subjects knew they would have to provide this information again (following scanning), and it is therefore likely that there were both explicit retrieval and re-encoding processes ongoing during the scanning phase. Such questions can be addressed in future studies. And, as the authors note, the parallel ERP study data closely resemble other work from this laboratory assessing retrieval processes. A novel and important aspect of this study is the ecologically valid means by which biographical information was provided to subjects. In social situations we are often simultaneously presented with a visual facial stimulus and auditory biographical information. The present study cleverly provides stimulus information in an experimental fashion that reasonably approximates these conditions. Future studies could move further in this direction by introducing competing tasks that might mimic some of the real-life distractors described above. Experimental studies are also hard pressed to match the motivations of their subjects with those that propel these real-life scenarios. For example, very different motivations likely underlie our desire to remember people in the first place. The present study used a monetary incentive to ensure behavioral compliance. Although we again could question the ecological validity of this, we can acknowledge that we often find ourselves at social engagements with only external motivation to actually encode and retrieve biographical information of the people we meet. (For example, imagine being at an office party of a significant other, where the pressure to meet and remember people comes solely from the wishes of the significant other.) It is, of course, impossible to know what the exact relevance is of brain activations in memory studies of this sort to the human examples of social interaction I have provided here. Indeed

Stress-activated p38 kinases control a plethora of functions and their dysregulation has been linked to development of steatosis, obesity, immune disorders and cancer. Therefore, they have been identified as potential targets for novel therapeutic strategies. There are four p38 family members (p38α, p38β, p38γ, and p38δ) that are activated by MKK3 and MKK6. Here we demonstrate that lack of MKK6 reduces the life span in mice. Longitudinal study of cardiac function in Mkk6-/- mice showed that young mice have cardiac hypertrophy which progresses to cardiac dilatation and fibrosis with age. Mechanistically, lack of MKK6 blunts p38α activation while causing MKK3-p38γ/δ hyperphosphorylation and increased mTOR signaling, resulting in cardiac hypertrophy. Cardiac hypertrophy in Mkk6-/- mice is reverted by knocking out either p38γ or p38δ, or by inhibiting mTOR pathway with rapamycin. In conclusion, we have identified a key role for the MKK3/6-p38γ/δ pathway in the development of cardiac hypertrophy, which has important implications for the clinical use of p38α inhibitors in the long-term treatment since they might result in cardiotoxicity.eLife’s Review ProcesseLife works to improve the process of peer review so that it more effectively conveys the assessment of expert reviewers to authors, readers and other interested parties. In the future we envision a system in which research is first published as a preprint and the outputs of peer review are the primary way research is assessed, rather than journal title.Our editorial process produces two outputs: i) an assessment by peers designed to be posted alongside a preprint for the benefit of the readers; ii) detailed feedback on the manuscript for the authors, including requests for revisions and suggestions for improvement.Therefore we want to change how we construct and write peer reviews to make them useful to both authors and readers in a way that better reflects the work you put into reading and thinking about a paper.eLife reviews now have three parts: An evaluation summary (in two or three sentences) that captures the major conclusions of the review in a concise manner, accessible to a wide audience.A public review that details the strengths and weaknesses of the manuscript before you, and discusses whether the authors’ claims and conclusions are justified by their data.A set of private recommendations for the authors that outline how you think the science and its presentation could be strengthened.All three sections will be used as the basis for an eLife publishing decision, which will, as always, be made after a consultation among the reviewers and editor. Each of the public reviews will be published (anonymously) alongside the preprint, together with a response from the authors if they choose. In the case of papers we reject after review, the authors can choose to delay posting until their paper has been published elsewhere.If this is your first time going through this new process, we ask that you take some time to read our Reviewer Guide, which discusses how we see each section will be used, what it should contain, and what we hope it accomplishes. And we remind you that, with the shift of reviews from private correspondence to public discourse, it is more important than ever that reviews are written in a clear and constructive manner appropriate for a public audience and mindful of the impact language choices might have on the authors.

It has extensively been documented that human memory exhibits a wide range of systematic distortions, which have been associated with resource constraints. Resource constraints on memory can be formalised in the normative framework of lossy compression, however traditional lossy compression algorithms result in qualitatively different distortions to those found in experiments with humans. We argue that the form of distortions is characteristic of relying on a generative model adapted to the environment for compression. We show that this semantic compression framework can provide a unifying explanation of a wide variety of memory phenomena. We harness recent advances in learning deep generative models, that yield powerful tools to approximate generative models of complex data. We use three datasets, chess games, natural text, and hand-drawn sketches, to demonstrate the effects of semantic compression on memory performance. Our model accounts for memory distortions related to domain expertise, gist-based distortions, contextual effects, and delayed recall., Author summary Human memory performs surprisingly poorly in many everyday tasks, which have been richly documented in laboratory experiments. While constraints on memory resources necessarily imply a loss of information, it is possible to do well or badly in relation to available memory resources. In this paper we recruit information theory, which establishes how to optimally lose information based on prior and complete knowledge of environmental statistics. For this, we address two challenges. 1, The environmental statistics is not known for the brain, rather these have to be learned over time from limited observations. 2, Information theory does not specify how different distortions of original experiences should be penalised. In this paper we tackle these challenges by assuming that a latent variable generative model of the environment is maintained in semantic memory. We show that compression of experiences through a generative model gives rise to systematic distortions that qualitatively correspond to a diverse range of observations in the experimental literature.

Consciousness is a central issue in cognitive neuroscience. To explain the relationship between consciousness and its neural correlates, various theories have been proposed. We still lack a formal framework that can address the nature of the relationship between consciousness and its physical substrates though. Here, we provide a novel mathematical framework of Category Theory (CT), in which we can define and study the “sameness” between “different” domains of phenomena such as consciousness and its neural substrates. CT was designed and developed to deal with the “relationships” between various domains of phenomena. We introduce three concepts of CT including (i) category; (ii) inclusion functor and expansion functor; and (iii) natural transformation between the functors. Each of these mathematical concepts is related to specific features in the neural correlates of consciousness (NCC). In this novel framework, we will examine two of the major theories of consciousness: integrated information theory (IIT) of consciousness and temporo-spatial theory of consciousness (TTC). These theories concern the structural relationships among structures of physical substrates and subjective experiences. The three CT-based concepts, introduced in this paper, unravel some basic issues in our search for the NCC; while addressing the same questions, we show that IIT and TTC provide different albeit complementary answers. Importantly, our account suggests that we need to go beyond a traditional concept of NCC including both content-specific and full NCC. We need to shift our focus from the relationship between “one” neuronal and “one” phenomenal state to the relationship between a structure of neural states and a structure of phenomenal states. We conclude that CT unravels and highlights basic questions about the NCC in general which needs to be met and addressed by any future neuroscientific theory of consciousness.Author summaryNeuroscience has made considerable progress in uncovering the neural correlates of consciousness (NCC). At the same time, recent studies demonstrated the complexity of the neuronal mechanisms underlying consciousness. To make further progress in the neuroscience of consciousness, we need proper mathematical formalization of the neuronal mechanisms potentially underlying consciousness. Providing a first tentative attempt, our paper addresses both by (i) pointing out the specific problems of and proposing a new approach to go beyond the traditional approach of the neural correlates of consciousness, and (ii) by recruiting a recently popular mathematical formalization, category theory (CT). With CT, we provide mathematical formalization of the broader neural correlates of consciousness by its application to two of the major theories, integrated information theory (IIT) and temporo-spatial theory of consciousness (TTC). Together, our CT-based mathematical formalization of the neural correlates of consciousness including its specification in the terms of IIT and TTC allows to go beyond the current concept of NCC in both mathematical and neural terms.

Strategies to mitigate climate change often center on clean technologies such as electric vehicles and solar panels, while the mitigation potential of a quality educational experience is rarely discussed. In this paper, we investigate the long-term impact that an intensive one-year university course had on individual carbon emissions by surveying students at least five years after having taken the course. A majority of course graduates reported pro-environmental decisions (i.e., type of car to buy, food choices) that can be attributed to experiences gained in the course. Furthermore, our carbon footprint analysis demonstrates that for the average course graduate, these decisions reduced their individual carbon emissions by 2.86 tons of CO2 per year. Focus group interviews identify that course graduates have developed a strong personal connection to climate change solutions, and this is realized in their daily behaviors and through their professional careers. The paper discusses in more detail the specific components of the course that are believed to be most impactful, and it shares preliminary outcomes from similar curriculum designs that are being used with K-12 students. Our analysis also demonstrates that if similar education programs were applied at scale, the potential reductions in carbon emissions would be of similar magnitude to other large-scale mitigation strategies such as rooftop solar or electric vehicles.

When listening to music, humans can easily identify and move to the beat. Numerous experimental studies have identified brain regions that may be involved with beat perception and representation. Several theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat, is the question of how we learn to generate and hold a beat. In this paper, we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech. Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus. The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates, but not exact information, of how well the beat generator spike times match those of a stimulus sequence. The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization. Our model makes generalizable predictions about the existence of asymmetries in the synchronization process, as well as specific predictions about resynchronization times after changes in stimulus tempo or phase. Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music, in a manner that is robust to changes in parameters and to the presence of noise., Author summary Music is integral to human experience and is appreciated across a wide range of cultures. Although many features distinguish different musical traditions, rhythm is central to nearly all. Most humans can detect and move along to the beat through finger or foot tapping, hand clapping or other bodily movements. But many people have a hard time “keeping a beat”, or say they have “no sense of rhythm”. There appears to be a disconnect between our ability to perceive a beat versus our ability to produce a beat, as a drummer would do as part of a musical group. Producing a beat requires beat generation, the process by which we learn how to keep track of the specific time intervals between beats, as well as executing the motor movement needed to produce the sound associated with a beat. In this paper, we begin to explore neural mechanisms that may be responsible for our ability to generate and keep a beat. We develop a computational model that includes different neurons and shows how they cooperate to learn a beat and keep it, even after the stimulus is removed, across a range of frequencies relevant to music. Our dynamical systems model leads to predictions for how the brain may react when learning a beat. Our findings and techniques should be widely applicable to those interested in understanding how the brain processes time, particularly in the context of music.

Contains fulltext : 180760.pdf (Publisher’s version ) (Open Access) New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence. 24 p.

Studies of resting state functional MRI (rs-fRMI) are increasingly focused on “dynamics”, or on those properties of brain activation that manifest and vary on timescales shorter than the scan’s full duration. This shift in focus has led to a flurry of interest in developing hypothesis testing frameworks and null models applicable to the dynamical setting. Thus far however, these efforts have been weakened by a number of crucial shortcomings that are outlined and discussed in this short paper. We focus here on aspects of recently proposed null models that, we argue, are poorly formulated relative to the hypotheses they are designed to test, i.e. their potential role in separating functionally relevant BOLD signal dynamics from noise or intermittent background and maintenance type processes is limited by factors that are fundamental rather than merely quantitative or parametric. In this short position paper, we emphasize that (1) serious care must be exercised in building null models for rs-fMRI dynamics from distributionally stationary univariate or multivariate timeseries, i.e. timeseries whose values are each independently drawn from one pre-specified probability distribution; and (2) measures such as kurtosis that quantify over-concentration of observed values in the far tails of some reference distribution may not be particularly suitable for capturing signal features most plausibly contributing to functionally relevant brain dynamics. Other metrics targeted, for example, at capturing the epochal temporal variation that contributes heavily to dynamic functional connectivity estimates and is and often taken as a signature of brain responsiveness to stimuli or experimental tasks, could play a more scientifically clarifying role. As we learn more about the phenomenon of functionally relevant brain dynamics and its imaging correlates, scientifically meaningful null hypotheses and well-tuned null models will naturally emerge. We also revisit the important concept of distributional stationarity, discuss how it manifests within realizations versus across multiple realizations, and provide guidance on the benefits and limitations of employing this type of stationarity in modeling the absence or functionally relevant temporal dynamics in resting state fMRI. We hope that the discussions herein are useful, and promote thoughtful consideration of these important issues.

Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. Historically, it has helped to localize specialized functions in the brain after brain damage, and it has gained new prominence through the arrival of modern optogenetic perturbation techniques that allow probing the functional contributions of neural circuit elements at unprecedented levels of detail.While inferences drawn from brain lesions are conceptually powerful, they face methodological difficulties due to the brain’s complexity. Particularly, they are challenged to disentangle the functional contributions of individual neural elements because many elements may contribute to a particular function, and these elements may be interacting anatomically as well as functionally. Therefore, studies of real-world data, as in clinical lesion studies, are not suitable for establishing the reliability of lesion approaches due to an unknown, potentially complex ground truth. Instead, ground truth studies of well-characterized artificial systems are required.Here, we systematically and exhaustively lesioned a small Artificial Neural Network (ANN) playing a classic arcade game. We determined the functional contributions of all nodes and links, contrasting results from single-element perturbations and perturbing multiple elements simultaneously. Moreover, we computed pairwise causal functional interactions between the network elements, and looked deeper into the system’s inner workings, proposing a mechanistic explanation for the effects of lesions.We found that not every perturbation necessarily reveals causation, as lesioning elements, one at a time, produced biased results. By contrast, multi-site lesion analysis captured crucial details that were missed by single-site lesions. We conclude that even small and seemingly simple ANNs show surprising complexity that needs to be understood for deriving a causal picture of the system. In the context of rapidly evolving multivariate brain-mapping approaches and inference methods, we advocate usingin-silicoexperiments and ground-truth models to verify fundamental assumptions, technical limitations, and the scope of possible interpretations of these methods.Author summaryThe motto“No causation without manipulation”is canonical to scientific endeavors. In particular, neuroscience seeks to find which brain elements are causally involved in cognition and behavior of interest by perturbing them. However, due to complex interactions among those elements, this goal has remained challenging.In this paper, we used an Artificial Neural Network as a ground-truth model to compare the inferential capacities of lesioning the system one element at a time against sampling from the set of all possible combinations of lesions.We argue for employing more exhaustive perturbation regimes since, as we show, lesioning one element at a time provides misleading results. We further advocate using simulated experiments and ground-truth models to verify the assumptions and limitations of brain-mapping methods.

Author(s): Lopopolo, Alessandro; Rabovsky, Milena | Abstract: The N400 component of the event related brain potential is widely used to investigate language and meaning processing. However, despite much research the component’s functional basis remains actively debated. Recent work showed that the update of the predictive representation of sentence meaning (semantic update, or SU) generated by the Sentence Gestalt model (McClelland, St. John, a Taraban, 1989) consistently displayed a similar pattern to the N400 amplitude in a series of conditions known to modulate this event-related potential. These results led (Rabovsky, Hansen, and McClelland,2018) to suggest that the N400 might reflect change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error. However, a limitation of this work is that the model was trained on a small artificial training corpus and thus could not be presented with the same naturalistic stimuli presented in empirical experiments. In the present study, we overcome this limitation and directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the SU generated by a Sentence Gestalt model trained on a large corpus of texts. The results reported in this paper corroborate the hypothesis that the N400 component reflects the change in a probabilistic representation of meaning after every word presentation. Further analyses demonstrate that the SU of the Sentence Gestalt model and the amplitude of the N400 are influenced similarly by the stochastic and positional properties of the linguistic input.

Science fairs offer potential opportunities for students to learn first-hand about the practices of science. Over the past six years we have been carrying out voluntary and anonymous surveys with regional and national groups of high school and post high school students to learn about their high school science fair experiences regarding help received, obstacles encountered, and opinions about the value and impact of science fair. Understanding what students think about science fairs will help educators make science fairs more effective learning opportunities. In this paper, we focus on the findings with two national groups of post high school students–undergraduate research fellows (SURF students) who did research at UT Southwestern Medical Center during 2014–2019 and undergraduates biology students attending the 2019 Howard Hughes Medical Institute Science Education Alliance (SEA) summer symposium. About 25% of the students who completed surveys indicated that they had participated in high school science fair, but more than half attended high schools where science fairs were unavailable. Effectively, 6 out of every 10 students participated in science fair if available. Students who could have participated in high school science fair but chose not to do so identified not enough time and coming up with their project idea as major reasons why not. About half the SURF students favored requiring non-competitive science fair regardless whether they themselves had participated in science fair. On the other hand, less than 1 in 5 thought that competitive science fair should be required. Introduction to the scientific process and general learning were mentioned most frequently as the reasons to require non-competitive science fair; these reasons were mentioned rarely in connection with competitive science fair. Unlike the national cohort of high school students we surveyed previously, who mostly did science fair in 9th and 10th grades, SURF students participated in science fair throughout high school and were twice as likely as high school students to have carried out science fair more than once. In conclusion, our findings suggest that participation of the undergraduate bioscience majors in high school science fairs occurs far more frequently than recognized previously and provide insights into how these successful college students (gauged by the fact that they are doing summer science research) view science fairs. The findings emphasize further the importance of incentivizing rather than requiring science fair participation, especially in 9th and 10th grades, and the potential value of developing non-competitive science fairs.

The development of noninvasive neuroimaging techniques such as functional magnetic resonance imaging was followed by a large volume of human neuroimaging studies of mental processes, mechanisms, and diseases. Due to the high volume of studies and the large number of journals, it is increasingly challenging for neuroscientists to review existing scholarly journals and find the most suitable journal to publish their studies. Therefore, this paper proposes a scholarly journal recommendation model for human neuroimaging studies called brain activation-based filtering (BAF). Based on the collective matrix factorization technique, BAF recommends journals relevant to the activated brain regions that are described in a given neuroimaging study. For instance, if ‘social brain’ regions such as the dorsomedial prefrontal cortex, precuneus, and temporoparietal junction are activated in a study, BAF recommends relevant social neuroscience journals (e.g., Social Cognitive and Affective Neuroscience). Five-fold cross-validation shows that BAF predicts journals with a reliable area under the curve score of 0.855. Furthermore, an interactive Google Colab notebook is offered to recommend relevant journals for a novel human neuroimaging study (https://github.com/JunsolKim/brain-activation-based-filtering).

This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be boosted for shared subspaces. Our findings broadly supported this hypothesis with either better performance on the second task if it shared the same subspace as the first, or positive correlations over task performance for shared subspaces. These empirical findings were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning and human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning., Author summary How does knowledge gained from previous experience affect learning of new tasks? This question of “Transfer Learning” has been addressed by teachers, psychologists, and more recently by researchers in the fields of neural networks and machine learning. Leveraging constructs from machine learning, we designed pairs of learning tasks that either shared or did not share a common subspace. We compared the dynamics of transfer learning in humans with those of a multitask neural network model, finding that human performance was consistent with a minimal capacity variant of the model. Learning was boosted in the second task if the same subspace was shared between tasks. Additionally, accuracy between tasks was positively correlated but only when they shared the same subspace. Our results highlight the roles of subspaces, showing how they could act as a learning boost if shared, and be detrimental if not.

Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computational efficiency is such that it can be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks., Author summary The importance of modeling to inform and support decision making is widely acknowledged. Understanding how to enhance contact tracing as part of the Testing-Tracing-Isolation (TTI) strategy for mitigation of COVID is a key public policy questions. Our work develops the SEIR-TTI model as an extension of the classic Susceptible, Exposed, Infected and Recovered (SEIR) model to include tracing of contacts of people exposed to and infectious with COVID-19. We use probabilistic argument to derive contact tracing rates within a compartmental model as aggregates of contact tracing at an individual level. Our adaptation is applicable across compartmental models for infectious diseases spread. We show that our novel SEIR-TTI model can accurately approximate the behaviour of mechanistic agent-based models at far less computational cost. The SEIR-TTI model represents an important addition to the theoretical methodology of modelling infectious disease spread and we anticipate that it will be immediately applicable to the management of the COVID-19 pandemic.

BackgroundDissemination of accurate health research information to patients and families has become increasingly important with the rise of the internet as a means of finding health information. However, the public faces several barriers to accessing research information; including paywalls and technical jargon. One method to bridge this gap between patients, families, and research is using lay summaries. SCAsource is an online knowledge translation platform where peer-reviewed research papers on ataxia are translated into lay summaries. This online platform was launched in September 2018, with the goal of making ataxia research more accessible and understandable to patients and families. A secondary goal is to provide opportunities for ataxia researchers to develop and hone their knowledge translation skills, altogether improving the quality of patient communication in the ataxia community.AimThe aim of this study was to measure the impact of SCAsource on its readers and volunteer contributors after one year of activity. This is to ensure SCAsource is meeting its goals of (1) improving access and understanding of ataxia research to lay audiences, and (2) improving knowledge translation skills of volunteer contributors.MethodsTwo online surveys were launched, one for readers and one for volunteers. Each survey had a combination of multiple-choice, Likert-scale type, and open-ended short-answer questions. Descriptive quantitative analysis was used for respondent characteristics and Likert-type data. A grounded theory coding approach was used to analyze narrative feedback data.ResultsWe found that SCAsource has mutually beneficial outcomes for both lay person readers and volunteer contributors. Readers have an increased understanding of ataxia research and access to up-to-date information on recent publications. Volunteers develop knowledge translation skills and have increased confidence in communicating results to lay audiences. Areas of improvement were identified to be incorporated into the platform.ConclusionWe demonstrated that SCAsource improves access to information and understanding of research to lay audiences, while providing opportunities for researchers to develop knowledge translation skills. This framework can potentially be used by other rare disease organizations to launch and evaluate their own knowledge translation websites.

Statistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from training sequences. However, PPM has limitations as a cognitive model: in particular, it has a perfect memory that weights all historic observations equally, which is inconsistent with memory capacity constraints and recency effects observed in human cognition. We address these limitations with PPM-Decay, a new variant of PPM that introduces a customizable memory decay kernel. In three studies—one with artificially generated sequences, one with chord sequences from Western music, and one with new behavioral data from an auditory pattern detection experiment—we show how this decay kernel improves the model’s predictive performance for sequences whose underlying statistics change over time, and enables the model to capture effects of memory constraints on auditory pattern detection. The resulting model is available in our new open-source R package, ppm (https://github.com/pmcharrison/ppm)., Author summary Humans hear a rich variety of sounds throughout everyday life, ranging from the basic (e.g. an alarm clock, a whistling kettle, an ambulance siren) to the complex (e.g. speech, music, birdsong). Understanding these sounds depends on an ability to detect and remember patterns in these sounds, patterns ranging from the two-tone oscillation of the ambulance siren to the classic four-chord progression of Western popular music. A key challenge in audition research is to develop effective computer models of these pattern-detection processes. The Prediction by Partial Matching model is one such model, originally developed for data compression, that learns statistical patterns of varying complexity from sequences of discrete symbols (e.g. ‘A, B, A, A, B, A, B’). In previous research this model has proved particularly effective for simulating listeners’ responses to music as well as other kinds of auditory sequences. However, the model is an unrealistic simulation of human cognition in that it possesses a perfect memory, unbounded in capacity, where historic events are recalled just as clearly as recent events. In this paper we therefore introduce a memory-decay component to the model, whereby the salience of historic auditory events decreases over time in line with the dynamics of human auditory memory. We present an experiment showing that this memory-decay model provides a natural account of experimental data from an auditory pattern detection task, explaining how human performance deteriorates as a function of the length of the patterns being detected and the speed at which they are played. Conversely, we also present two simulation studies showing that this memory-decay component can improve pattern detection in auditory environments whose statistical structure changes dynamically over time. These studies indicate the potential benefit of incorporating memory constraints into statistical models of auditory pattern detection, and highlight how these memory constraints can both impair and improve pattern detection, depending on the environment.

In this paper, we present a functional spiking-neuron model of human working memory (WM). This model combines neural firing for encoding of information with activity-silent maintenance. While it used to be widely assumed that information in WM is maintained through persistent recurrent activity, recent studies have shown that information can be maintained without persistent firing; instead, information can be stored in activity-silent states. A candidate mechanism underlying this type of storage is short-term synaptic plasticity (STSP), by which the strength of connections between neurons rapidly changes to encode new information. To demonstrate that STSP can lead to functional behavior, we integrated STSP by means of calcium-mediated synaptic facilitation in a large-scale spiking-neuron model and added a decision mechanism. The model was used to simulate a recent study that measured behavior and EEG activity of participants in three delayed-response tasks. In these tasks, one or two visual gratings had to be maintained in WM, and compared to subsequent probes. The original study demonstrated that WM contents and its priority status could be decoded from neural activity elicited by a task-irrelevant stimulus displayed during the activity-silent maintenance period. In support of our model, we show that it can perform these tasks, and that both its behavior as well as its neural representations are in agreement with the human data. We conclude that information in WM can be effectively maintained in activity-silent states by means of calcium-mediated STSP., Author summary Mentally maintaining information for short periods of time in working memory is crucial for human adaptive behavior. It was recently shown that the human brain does not only store information through neural firing–as was widely believed–but also maintains information in activity-silent states. Here, we present a detailed neural model of how this could happen in our brain through short-term synaptic plasticity: rapidly adapting the connection strengths between neurons in response to incoming information. By reactivating the adapted network, the stored information can be read out later. We show that our model can perform three working memory tasks as accurately as human participants can, while using similar mental representations. We conclude that our model is a plausible and effective neural implementation of human working memory.

Decision making relies on adequately evaluating the consequences of actions on the basis of past experience and the current physiological state. A key role in this process is played by the basal ganglia, where neural activity and plasticity are modulated by dopaminergic input from the midbrain. Internal physiological factors, such as hunger, scale signals encoded by dopaminergic neurons and thus they alter the motivation for taking actions and learning. However, to our knowledge, no formal mathematical formulation exists for how a physiological state affects learning and action selection in the basal ganglia. We developed a framework for modelling the effect of motivation on choice and learning. The framework defines the motivation to obtain a particular resource as the difference between the desired and the current level of this resource, and proposes how the utility of reinforcements depends on the motivation. To account for dopaminergic activity previously recorded in different physiological states, the paper argues that the prediction error encoded in the dopaminergic activity needs to be redefined as the difference between utility and expected utility, which depends on both the objective reinforcement and the motivation. We also demonstrate a possible mechanism by which the evaluation and learning of utility of actions can be implemented in the basal ganglia network. The presented theory brings together models of learning in the basal ganglia with the incentive salience theory in a single simple framework, and it provides a mechanistic insight into how decision processes and learning in the basal ganglia are modulated by the motivation. Moreover, this theory is also consistent with data on neural underpinnings of overeating and obesity, and makes further experimental predictions., Author summary Behaviour is made of decisions that are based on the evaluation of costs and benefits of potential actions in a given situation. Actions are usually generated in response to reinforcement cues which are potent triggers of desires that can range from normal appetites to compulsive addictions. However, learned cues are not constant in their motivating power. Food cues are more potent when you are hungry than when you have just finished a meal. These changes in cue-triggered desire produced by a change in biological state present a challenge to many current computational models of motivation and learning. Here, we demonstrate concrete examples of how motivation can instantly modulate reinforcement values and actions; we propose an overarching framework of learning and action selection based on maintaining the physiological balance to better capture the dynamic interaction between learning and physiology that controls the incentive salience mechanism of motivation for reinforcements. These models provide a unified account of state-dependent learning of the incentive value of actions and selecting actions according to the learned positive and negative consequences of those actions and with respect to the physiological state. We propose a biological implementation of how these processes are controlled by an area in the brain called the basal ganglia, which is associated with error-driven learning.

In this hypothesis paper we argue that when driven by example behavior, a simple Hebbian learning mechanism can form the core of a computational theory of learning that can support both low level learning and the development of human level intelligence. We show that when driven by example behavior Hebbian learning rules can support semantic, episodic and procedural memory. For humans, we hypothesize that the abilities to manipulate an off-line world model and to abstract using language allow for the generation and communication of rich example behavior, and thereby support human learning and a gradual increase of collective human intelligence across generations. We also compare the properties of Example Based Hebbian (EBH) learning with those of backpropagation-based learning and argue that the EBH mechanism is more consistent with observed characteristics of human learning.

Drosophila melanogaster are known to live in a social but cryptic world of touch and odours, but the extent to which they can perceive and integrate visual information is a hotly debated topic. Some researchers fixate on the limited resolution of D. melanogaster’s optics, other’s on their seemingly identical appearance; yet there is evidence of individual recognition and surprising visual learning in flies. Here, we apply machine learning and show that individual D. melanogaster are visually distinct. We also use the striking similarity of Drosophila’s visual system to current convolutional neural networks to theoretically investigate D. melanogaster’s capacity for visual understanding. We find that, despite their limited optical resolution, D. melanogaster’s neuronal architecture has the capability to extract and encode a rich feature set that allows flies to re-identify individual conspecifics with surprising accuracy. These experiments provide a proof of principle that Drosophila inhabit in a much more complex visual world than previously appreciated.Author summaryIn this paper, we determine a proof of principle for inter-individual recognition in two parts; is there enough information contained in low resolution pictures for inter-fly discrimination, and if so does Drosophila’s visual system have enough capacity to use it. We show that the information contained in a 29×29 pixel image (number of ommatidia in a fly eye) is sufficient to achieve 94% accuracy in fly re-identification. Further, we show that the fly eye has the theoretical capacity to identify another fly with about 75% accuracy. Although it is unlikely that flies use the exact algorithm we tested, our results show that, in principle, flies may be using visual perception in ways that are not usually appreciated.

The neurobiology investigation of language seems limited by the impossibility to link directly linguistic computations with neural computations. To address this issue, we need to explore the hierarchical interconnections between the investigated fields trying to develop an inter-field theory. Considerable research has realized that event-related fluctuations in rhythmic, oscillatory EEG/MEG activity may provide a new window on the dynamics of functional neuronal networks involved in cognitive processing. Accordingly, this paper aims to outline a formal proposal on neuronal computation and representation of syntactic structures within the oscillatory neuronal dynamics. I briefly present the nature of event-related oscillations and how they work on the base of synchronization and de-synchronization processes. Then, I discuss some theoretical premises assuming that reentrant (hierarchical) properties of synchronized oscillatory rhythms constitute the biological endowment that allow the development of language in humans when exposed to appropriate inputs. The main rhythms involved in language and speech processing are examined: i.e. theta, alpha, beta, and gamma bands. A possible formal representation of the syntactic structures on the base of these oscillatory rhythms is discussed: in this model, the theta-gamma rhythms are cross-frequency coupled into the alpha-gamma-beta and into the gamma-beta-theta rhythms to generate the sentence along reentrant cortico-thalamic pathways through Merge, Label and Move operations. Finally, I present few conclusive remarks within an evolutionary perspective.“…The whole burden of philosophy seems to consist in this, from the phenomena of motions to investigate the forces of nature, and from these forces to demonstrate the other phenomena.”(Newton, 1687/1726)

Cognitive neuroscience, especially in the fields of learning and decision-making, is witnessing the blossoming of computational model-based analyses. Several methodological and review papers have indicated how and why candidate models should be compared by trading off their ability to predict the data as a function of their complexity. However, the importance of simulating candidate models has been so far largely overlooked, which entails several drawbacks and leads to invalid conclusions. Here we argue that the analysis of model simulations is often necessary to support the specific claims about behavioral function that most of model-based studies make. We defend this argument both informally by providing a large-scale (N>300) review of recent studies, and formally by showing how model simulations are necessary to interpret model comparison results. Finally, we propose guidelines for future work, which combine model comparison and simulation.

Neural models of a distributed system for face perception implicate a network of regions in the ventral visual stream for recognition of identity. Here, we report an fMRI neural decoding study in humans that shows that this pathway culminates in a right inferior frontal cortex face area (rIFFA) with a representation of individual identities that has been disentangled from variable visual features in different images of the same person. At earlier stages in the pathway, processing begins in early visual cortex and the occipital face area (OFA) with representations of head view that are invariant across identities, and proceeds to an intermediate level of representation in the fusiform face area (FFA) in which identity is emerging but still entangled with head view. Three-dimensional, view-invariant representation of identities in the rIFFA may be the critical link to the extended system for face perception, affording activation of person knowledge and emotional responses to familiar faces.Significance StatementIn this fMRI decoding experiment, we address how face images are processed in successive stages to disentangle the view-invariant representation of identity from variable visual features. Representations in early visual cortex and the occipital face area distinguish head views, invariant across identities. An intermediate level of representation in the fusiform face area distinguishes identities but still is entangled with head view. The face-processing pathway culminates in the right inferior frontal area with representation of view-independent identity. This paper clarifies the homologies between the human and macaque face processing systems. The findings show further, however, the importance of the inferior frontal cortex in decoding face identity, a result that has not yet been reported in the monkey literature.

To the dismay of folks in my lab looking for shelf space for storage, I'm one of those PIs who has saved and treasured his copies of the RNA journal since Volume 1, Issue 1. However, let me put to rest the rumors that I used to read RNA articles to my son Jeremy at bedtime every evening. I never did such a thing in his formative years (he was old enough to read 'em himself….). Back in the infancy of this now well-established publication, I ran a joint RNA journal club with the Lutz lab in which we assigned journals to participants and would survey the field (and eat some delicious baked goods) every week. Being responsible for the RNA journal was both a joy and a curse as you always had interesting papers to present when an issue came out, but how could you chose which of the articles to focus on to summarize for the group? Congratulations to the RNA journal for 20 years of making journal clubs a great place to spend a Friday afternoon. Working in the RNA field has been quite an exciting ride over the last 20 years, hasn't it? A PubMed search using the term “DNA” shows that the number of indexed publications increased from 38,324 to 59,543 per calendar year since the RNA journal began in 1995, an increase of ∼55%. Publications that appear using the search term “RNA”, however, increased from 19,817 to 45,563 per calendar year—an increase of almost 130%. So despite major advances in DNA sequencing and genomics, one can use these stats to argue that our RNA field is expanding at more than twice the rate of the DNA field. There have been numerous significant advances in the realm of RNA biology over the last 20 years that have fueled this growth. These include the truly groundbreaking advances such as the discovery of RNA interference/the impact of small RNA regulators in cell biology, the discovery and characterization of riboswitches, major advances in elucidating RNA structures, and the determination of mechanisms and impact of regulated RNA processing and stability. Discoveries such as the growing impact of long non-coding RNAs, the networking of RNA-based processes and the rising role of RNA modifications in cell biology are also providing a vast amount of fuel to the field. The impact of the RNA biology that we know and love has also been compounded by a significant increase in its incorporation into many interdisciplinary arenas of investigation (e.g., stem cell biology, neuroscience, etc.). We've clearly forced many scientists (some kicking and screaming) to come out of their experimental comfort zone and enter the RNA realm in their research. What might the next 20 years have in store for the field of RNA Biology? Here's a list (in no particular order of priority) of questions that are among the key areas that need to be addressed in the field. (1) What is the function of the “dark matter” of the eukaryotic transcriptome? While significant work is already underway in this area, a thorough understanding of the biological functions of the plethora of non-coding transcripts has a very good chance of providing novel paradigms and groundbreaking innovation to our understanding of the molecular biology of the cell. (2) What are the components and dynamics of the messenger RNP? Our lack of understanding of what an mRNP truly consists of and how it dynamically changes through the life of an mRNA is, in my opinion, a major rate limiting factor to future progress in the field. This is one of the key remaining frontiers in the study of fundamental aspects of gene expression. (3) What is the full range and relevance of RNA structures? RNA structure is an inherent part of all RNA biology, yet we still only have a rather rudimentary understanding of the forms assumed by most RNAs. Continued advancement in the area of RNA structural biology will undoubtedly deliver novel insights for both basic and applied science applications. (4) What is the role of higher order RNA-containing structures inside cells? We badly need to continue and expand work to generate an in-depth characterization of the array of RNA granules that form in cells during development and in response to a variety of external stimuli. These oftentimes elusive RNA structures undoubtedly play key roles in numerous aspects of cell biology and a better understanding of how and why they form should provide significant innovation. Improvements in single cell analyses should play a key role in this area (and several other areas on this list). (5) Are there additional significant nuances to how translation is regulated? While we clearly have a strong handle on the mechanistic basics of translation, my sense is that with so much cellular energy going into this process, the likelihood of additional regulation over and above what we are already aware of is high. The implications of ribosomal subpopulations, the continued expansion of the impact of different RBPs and RNA-mediators on translational efficiency, and the networking of translation with other processes also indicate that this field may be poised for some major breakthroughs. (6) What are the biological impacts of “social” RNA? Every living organism makes and secretes highly stable and effective RNases into the environment undoubtedly for reasons other than just to make our experimental lives frustrating at times. Thus understanding the impact of RNA-based intercellular and inter-organismal communication may prove extremely interesting. (7) What is the biological significance of regulatable RNA modification? Building off of recent work on the m6A modification of mRNA, future studies are likely to provide an exciting picture of the impact of RNA modifications on cell biology. (8) What is the ultimate role of RNA and post-transcriptional processes in the overall regulation of gene expression of complex cellular pathways? Understanding the molecular basis for eukaryotic RNA regulons may change our appreciation of the regulation of many key aspects of cell biology. (9) Does RNA biology play a more notable role in mechanisms of disease pathogenesis? In many cases, the molecular explanation of cytopathology and other pathological manifestations of viral diseases are not very well understood. As our understanding of the impact of RNA-based processes in cell biology increases, it would not surprise me if our understanding of pathogenic mechanisms increases as viruses disrupt the homeostasis of RNA/RBPs in the cell. (10) Can we generate game-changing RNA aptamers? I would be very surprised if RNA aptamer technologies did not advance to develop key reagents for basic research, diagnostic and therapeutic applications. This aspect of RNA biology should be a major player in synthetic biology in the coming years. Let me finish with some closing comments regarding the RNA journal. In this age of electronic publishing, we cannot understate the value and prestige of a Society-backed journal. The journal was established at a time when most biological scientists had just started to appreciate the nuances and potential impact of RNA-based regulation due to its interdisciplinary impacts. However without a journal that the field could call its own, RNA biology remained in many researchers’ minds as a footnote to DNA and protein-based processes rather than an independent, vibrant discipline. I have always felt that the RNA journal sets the benchmark for studies in RNA biology. As an editor for a review journal in the RNA field, frankly I use the RNA journal as the baseline to determine the activity in the field in order to put impact factors and other publication metrics into the appropriate perspective. I'd like to congratulate Tim, Ann Marie, the folks at CSHL Press and all of the RNA editors/editorial board past and present for a job well done. I would encourage everyone to buy these hard working individuals a beverage or two of their choice in Madison at the RNA Society Meeting this Spring. The journal's past is something to be extremely proud of and the future is brighter than a Spinach aptamer. The inane pun “you can't spell ‘jouRNAl’ without RNA” is actually a very true statement for this field.

Molecular genetic approaches have facilitated our understanding of numerous diseases and provided important insights into the neurobiological basis of various behaviors. Until recently, the complex behavioral and physiological adaptation known as sleep has been impervious to such approaches. In the last two years, however, a familiar theme has been played out in the sleep field: The discovery of the genetic basis of an animal model of a human disorder has implications both for the treatment of patients and for the neural basis of a specific behavior. In this issue, the paper by Hungs et al. provides the latest chapter in this fascinating and rapidly unfolding story.

I recall as a graduate student being excited reading the first papers describing the discovery of RNA catalysis as well as the commentary on the enzymological and evolutional implications that followed. I feel a bit sorry for students in my laboratory who share a fascination with ribozymes, but for whom RNA catalysis is a fact learned in undergraduate biochemistry class. This book, however, through a series of expert reviews, explores the forefront of ribozyme research while evoking the excitement of those early discoveries. One reason for this attribute is that this is an update of a volume edited by Drs. Eckstein and Lilley 10 years ago, and their scholarly perspective on how the field has grown and developed is reflected in the choice of themes and the spirit of the volume. The foremost example is the introduction by Tom Cech, who received the Nobel Prize along with Sid Altman for the discovery of RNA catalysts, in which he contrasts predictions made on the structure and mechanism of ribozymes at the time of their discovery to the “modern” perspective. The first third of the volume discusses small self-cleaving ribozyme structure and function with appropriate emphasis on the importance of acid–base catalysis. A defining characteristic of ribozyme enzymology that is reflected in most of the reviews, but especially so in those on small ribozymes, is the facility for probing individual functional groups by site-specific modification. The well-developed field of nucleotide chemistry combined with the facility of solid phase synthesis permits structure–function studies to be done at essentially atomic resolution. One of the most exciting areas of development is exploring how RNAs facilitate proton transfer resulting in transition state stabilization. Detailed mechanistic aspects of proton transfer can be difficult to digest, but Phil Bevilacqua presents a clear and concise overview of proton transfer chemistry (scandalously invoking William Jencks' “libido” rule in the process), the driving forces for pKa shifting, and considerations for the quantitative contribution of proton transfer to rate enhancement. This information serves as a context for the case by case studies of individual small ribozymes that are explored in more detail in subsequent chapters. Like all fields of science there are a few great “stories” in which competing or exclusive theories arise that are later insightfully resolved by elegant experimental solutions that in hindsight seem obvious. The evolution of understanding the structure and function of the hammerhead ribozyme is such a narrative, and Bill Scott gives the insider's view of how it unfolded (or folded, eventually). Dominic Lambert and John Burke extend this theme by focusing in greater detail on analysis and modeling of the hammerhead ribozyme active site. Subsequent chapters by Joe Piccirilli and colleagues and by D.M.J. Lilley integrate the structural and functional information leading to the current mechanistic framework for the HDV, hairpin, and VS ribozymes. Highlighted for veterans of ribozyme research and newbies alike in these chapters is the difficulty in pinning down the specific role of a functional group as an acid or base catalyst. The high degree of linkage between conformational dynamics and observed rates of catalysis is also tied in well. Together with related chapters on relative newcomers, the glmS riboswitch riboyme by Link and Breaker and mammalian self-cleaving ribozymes by Andrej Luptak and Jack Szostak, the essential features of small ribozyme research relating to catalysis are well covered. A second major section is devoted to chapters exploring the large ribozymes, RNase P RNA and Group I and Group II introns. Here, the coordination of new structural perspectives with incisive biochemical analyses to understand the mechanism takes center stage with a particular focus on the role of active site metal ions. For the bacterial RNase P ribozyme the big story is the elucidation of three-dimensional structures of its RNA and protein subunits. Norm Pace, himself a direct participant in the birth of ribozymology, and his colleagues review substrate positioning and active site-probing data in the context of this new structural perspective (with a characteristically healthy dose of phyolgenetic comparative analyses). For the Group I intron ribozymes Barb Golden provides a balanced and entertaining review of the integration of high-resolution structures in the development of models for the active site of this ribozyme. Essential material relating the coordination of biochemical studies, such as thiophilic metal rescue analyses, with active site metal ion interactions in high-resolution structures of the active site is well covered here. This serves as the basis for interesting explorations of what constitutes a catalytic site in RNA and how complex RNA catalytic mechanisms may have evolved. Group II introns are given a comprehensive treatment by Anna Pyle from their complex secondary and tertiary structures revealed by insightful biochemical and comparative sequence analyses to current models for active site architecture and the role of protein maturases. Among the gems found throughout these chapters is the inclusion of a not entirely tongue-in-cheek argument for GII introns as “tiny living things.” It makes me wonder whether the slow pace of research in my laboratory could be due to the wicked designs of these creatures after all. A strength of the volume, which makes it both interesting to the expert and highly useful for the newcomer, is the inclusion of chapters on the spliceosome and ribosome. The spliceosome has yet to receive widespread recognition as a ribozyme (indeed, the title of the chapter by Dipali Sashital and Sam Butcher is punctuated by a question mark). However, many of the fundamental properties of ribozymes have proven useful in understanding spliceosomal RNA function and dynamics and defining the features that a spliceosomal active site must possess—features that are expertly discussed in terms of emerging high-resolution structures of portions of snRNAs. Ribosome enzymology is another emergent field, and the chapter by Marina Rodnina adroitly outlines how the mechanism of the peptidyl transferase center is defined by the fundamental chemistry of aminolysis. Indeed, this chapter is exemplary from the standpoint of building up an understanding of mechanisms from first (chemical) principles. Coverage of ribozyme structure and mechanism would not be complete without consideration of RNA folding, given the obvious linkage between structure and function and the ability of RNA to assume multiple stable conformations. Others have pointed to the fact that this may be a property that is useful for RNA function in multistep reactions and in achieving specificity. RNA folding is, in its own right, a subject worthy of an entire volume, but again, for the student or chemist/biologist entering the field, the main issues are defined and expertly explored by Sarah Woodson and Prashanth Rangan. A strength of their chapter is that the authors comprehensively cover the most useful biophysical and molecular approaches, making it an ideal roadmap for students and researchers who are dissecting conformational changes in other systems. Inevitably, there are areas for which a biased reader (such as me) would desire greater coverage. Given the complexity of the issues involved, one theme that could be developed further in an integrative chapter like the one by Bevilacqua is the interaction of RNA and metal ions. Nonetheless, chapters by Golden, Pace, Pyle and others cover the important issues and cite comprehensive reviews that are excellent. Also, further information on the chemistry of phosphoryl transfer would be useful since diagrams of ribozyme chemical mechanism included throughout the volume show some interesting variety in the bond order, charge, and protonation states. In sum, this is a great resource for advanced students and researchers getting a grip on the major issues and advances in the field. Indeed, an excellent and concise primer to the key issues and themes addressed by the volume is included as Chapter 1. It is also valuable as a source of comprehensive reviews of the major experimental systems to use for reference and as an entry into the literature for these systems. For the new students in our laboratory, it is now required reading. Mike Harris Center for RNA Molecular Biology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA; e-mail: ude.urwc@hem; fax: (216) 368-2010.